Calorimeter surrogates

The Kaggle calorimeter challenge uses generative AI to produce a surrogate for the Monte Carlo calculation of a calorimeter response to an incident particle (ATLAS data at LHC calculated with GEANT4).

Overview

The Kaggle calorimeter challenge uses generative AI to produce a surrogate for the Monte Carlo calculation of a calorimeter response to an incident particle (ATLAS data at LHC calculated with GEANT4). Variational Auto Encoders, GANs, Normalizing Flows, and Diffusion Models. We also have a surrogate using a Quantum Computer (DWAVE) annealer to generate random samples. We have identified four different surrogates that are available openly from Kaggle and later submissions.

Figure 1: CaloChallenge Dataset.

Details

Accurate simulation plays a crucial role in particle physics by bridging theoretical models with experimental data to uncover the universe’s fundamental properties. At the Large Hadron Collider (LHC), simulations based on Monte Carlo methods model the interactions of billions of particles, including complex calorimeter shower events—cascades of secondary particles produced when high-energy particles hit detector materials. The widely-used Geant4 1 simulation toolkit provides highly detailed physics-based simulations, but its computational cost is extremely high, making up over 75% of the total simulation time 2. With the upcoming High-Luminosity LHC (HL-LHC) 3,4 upgrade in 2029, the collider will generate larger datasets with higher precision requirements, significantly increasing the demand for computational resources. To mitigate this, researchers are exploring generative models commonly used in image and text generation—as surrogate models that can generate realistic calorimeter showers at a fraction of the computational cost. In recent years, several approaches based on Generative Adversarial Networks(GAN) 5, 6, 7, 8, 9, 10, Diffusion 11 12, 13, 14, 15, 16, 17, 18, 19, Variational Autoencoders (VAEs) 20, 21, 22, 23, 24, 25, 26, 27, 28 and Normalizing Flows 29, 30, 31, 32, 33, 34, 35 have been proposed. However, evaluating these models remains challenging because the physical characteristics of calorimeter showers differ significantly from traditional image- and text-based data. 36, 37 conducted a rigorous evaluation of these generative models using standard datasets and a diverse set of metrics derived from physics, computer vision, and statistics. Although 36 sheds light on the existent correlations between layers, they do not quantify correlations between layers and voxels. In this work, we propose Correlation Frobenius Distance (CFD), an evaluation metric for generative models of calorimeter shower simulation. This metric measures how the consecutive layers and voxels of generated samples are correlated with each other compared to Geant4 samples. CFD helps evaluate the consistency of energy deposition patterns across layers, capturing the spatial correlations in the calorimeter shower. Lower CFD values indicate that the generated samples better preserve the correlations observed in Geant4 simulations. We compared four different models (CaloDream 19, CaloScore v2 18, CaloDiffusion 27, and CaloINN 33) on Dataset 2 38 from CaloChallenge 2022 13 for CFD, our observation reveals that CaloDream can capture correlations between consecutive layers and voxels the best. Furthermore, we explored the impact of using full versus mixed precision modes during inference for CaloDiffusion. Our observation shows that mixed precision inference does not speed up inference for Dataset 1 39 and Dataset 2 39.​ However, it significantly improves inference time for Dataset 3 39, without compromising performance.​ The Code is available in GitHub at 40.

Additional relevant references include:

41, 42,

Team contributed refernces include

43, 44, 45, 46,

References

Team contributed refernces are marked in bold

  1. Agostinelli, Sea, et al. “GEANT4—a simulation toolkit.” Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment 506.3 (2003): 250-303. ↩︎

  2. Muškinja, Miha, John Derek Chapman, and Heather Gray. “Geant4 performance optimization in the ATLAS experiment.” EPJ Web of Conferences. Vol. 245. EDP Sciences, 2020. ↩︎

  3. “New Schedule for CERN’s Accelerators.” CERN, 5 Dec. 2023, [https://home.cern/news/news/accelerators/new-schedule-cerns-accelerators]:(https://home.cern/news/news/accelerators/new-schedule-cerns-accelerators). Accessed 28 Feb. 2025. ↩︎

  4. “Computing at CERN.” CERN, https://home.web.cern.ch/science/computing. Accessed 28 Feb. 2025. ↩︎

  5. ATLAS collaboration. “Fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks.” ATLAS PUB Note, CERN, Geneva (2020). ↩︎

  6. Ghosh, Aishik, and ATLAS collaboration. “Deep generative models for fast shower simulation in ATLAS.” Journal of Physics: Conference Series. Vol. 1525. No. 1. IOP Publishing, 2020. ↩︎

  7. Giannelli, Michele Faucci, and Rui Zhang. “CaloShowerGAN, a generative adversarial network model for fast calorimeter shower simulation.” The European Physical Journal Plus 139.7 (2024): 597. ↩︎

  8. Paganini, Michela, Luke de Oliveira, and Benjamin Nachman. “Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters.” Physical review letters 120.4 (2018): 042003. ↩︎

  9. de Oliveira, Luke, Michela Paganini, and Benjamin Nachman. “Learning particle physics by example: location-aware generative adversarial networks for physics synthesis.” Computing and Software for Big Science 1.1 (2017): 4. ↩︎

  10. Paganini, Michela, Luke de Oliveira, and Benjamin Nachman. “CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks.” Physical Review D 97.1 (2018): 014021. ↩︎

  11. Acosta, Fernando Torales, et al. “Comparison of point cloud and image-based models for calorimeter fast simulation.” Journal of Instrumentation 19.05 (2024): P05003. ↩︎

  12. Amram, Oz, and Kevin Pedro. “Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation.” Physical Review D 108.7 (2023): 072014. ↩︎

  13. Buhmann, Erik, et al. “CaloClouds: fast geometry-independent highly-granular calorimeter simulation.” Journal of Instrumentation 18.11 (2023): P11025. ↩︎ ↩︎

  14. Buhmann, Erik, et al. “CaloClouds II: ultra-fast geometry-independent highly-granular calorimeter simulation.” Journal of Instrumentation 19.04 (2024): P04020. ↩︎

  15. Cresswell, Jesse C., and Taewoo Kim. “Scaling Up Diffusion and Flow-based XGBoost Models.” arXiv preprint arXiv:2408.16046 (2024). ↩︎

  16. Madula, T., and V. M. Mikuni. “CaloLatent: Score-based Generative Modelling in the Latent Space for Calorimeter Shower Generation NeurIPS Workshop on Machine Learning and the Physical Sciences URL https://ml4physicalsciences. github. io/2023/files.” NeurIPS_ ML4PS_2023_19. pdf (2023). ↩︎

  17. Mikuni, Vinicius, and Benjamin Nachman. “Score-based generative models for calorimeter shower simulation.” Physical Review D 106.9 (2022): 092009. ↩︎

  18. Mikuni, Vinicius, and Benjamin Nachman. “CaloScore v2: single-shot calorimeter shower simulation with diffusion models.” Journal of Instrumentation 19.02 (2024): P02001. ↩︎ ↩︎

  19. Favaro, Luigi, et al. “CaloDREAM–Detector Response Emulation via Attentive flow Matching.” arXiv preprint arXiv:2405.09629 (2024). ↩︎ ↩︎

  20. Cresswell, Jesse C., et al. “CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds.” arXiv preprint arXiv:2211.15380 (2022). ↩︎

  21. Buhmann, Erik, et al. “Decoding photons: Physics in the latent space of a BIB-AE generative network.” EPJ Web of Conferences. Vol. 251. EDP Sciences, 2021. ↩︎

  22. Buhmann, Erik, et al. “Getting high: High fidelity simulation of high granularity calorimeters with high speed.” Computing and Software for Big Science 5.1 (2021): 13. ↩︎

  23. Diefenbacher, Sascha, et al. “New angles on fast calorimeter shower simulation.” Machine Learning: Science and Technology 4.3 (2023): 035044. ↩︎

  24. Salamani, Dalila, Anna Zaborowska, and Witold Pokorski. “MetaHEP: Meta learning for fast shower simulation of high energy physics experiments.” Physics Letters B 844 (2023): 138079. ↩︎

  25. Abhishek, Abhishek, et al. “CaloDVAE: Discrete variational autoencoders for fast calorimeter shower simulation.” arXiv preprint arXiv:2210.07430 (2022). ↩︎

  26. Caloqvae: Simulating high-energy particle calorimeter interactions using hybrid quantum-classical generative models ↩︎

  27. Hoque, Sehmimul, et al. “CaloQVAE: Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models.” The European Physical Journal C 84.12 (2024): 1-7. ↩︎ ↩︎

  28. Lu, Ian, et al. “Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions.” arXiv preprint arXiv:2412.04677 (2024). ↩︎

  29. Krause, Claudius, and David Shih. “Fast and accurate simulations of calorimeter showers with normalizing flows.” Physical Review D 107.11 (2023): 113003. ↩︎

  30. Krause, Claudius, Ian Pang, and David Shih. “CaloFlow for CaloChallenge dataset 1.” SciPost Physics 16.5 (2024): 126. ↩︎

  31. Buckley, Matthew R., et al. “Inductive simulation of calorimeter showers with normalizing flows.” Physical Review D 109.3 (2024): 033006. ↩︎

  32. Diefenbacher, S., et al. “L2LFlows: generating high-fidelity 3D calorimeter images (2023).” arXiv preprint arXiv:2302.11594 18: P10017. ↩︎

  33. Ernst, Florian, et al. “Normalizing flows for high-dimensional detector simulations.” arXiv preprint arXiv:2312.09290 (2023). ↩︎ ↩︎

  34. Liu, Junze, et al. “Geometry-aware autoregressive models for calorimeter shower simulations.” arXiv preprint arXiv:2212.08233 (2022). ↩︎

  35. Schnake, Simon, Dirk Krücker, and Kerstin Borras. “CaloPointFlow II generating calorimeter showers as point clouds.” arXiv preprint arXiv:2403.15782 (2024). ↩︎

  36. Ahmad, Farzana Yasmin, Vanamala Venkataswamy, and Geoffrey Fox. “A comprehensive evaluation of generative models in calorimeter shower simulation.” arXiv preprint arXiv:2406.12898 (2024). ↩︎ ↩︎

  37. Krause, Claudius, et al. “Calochallenge 2022: A community challenge for fast calorimeter simulation.” arXiv preprint arXiv:2410.21611 (2024). ↩︎

  38. Ahmad, F. Y. Generated Samples of Dataset 2 from Calochallenge_2022. Zenodo, 17 Feb. 2025, doi:10.5281/zenodo.14883798. ↩︎

  39. CaloChallenge Homepage*, calochallenge.github.io/homepage/. Accessed 3 Mar. 2025. ↩︎ ↩︎ ↩︎

  40. GitHub: https://github.com/Aaheer17/Benchmarking_Calorimeter_Shower_Simulation_Generative_AI/tree/main ↩︎

  41. Michele Faucci Giannelli, Gregor Kasieczka, Claudius Krause, Ben Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Fast calorimeter simulation challenge 2022 - dataset 1,2 and 3 [data set]. zenodo., https://doi.org/10.5281/zenodo.8099322, https://doi.org/10.5281/zenodo.6366271, https://doi.org/10.5281/zenodo.6366324 (2022). ↩︎ ↩︎

  42. ATLAS Collaboration, ATLAS software and computing HL-LHC roadmap, Tech. Rep. (Technical report, CERN, Geneva. http://cds.cern.ch/record/2802918, 2022). ↩︎ ↩︎ ↩︎

  43. Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions, J Quetzalcoatl Toledo-Marin, Sebastian Gonzalez, Hao Jia, Ian Lu, Deniz Sogutlu, Abhishek Abhishek, Colin Gay, Eric Paquet, Roger Melko, Geoffrey C Fox, Maximilian Swiatlowski, Wojciech Fedorko, 2024/10/30 arXiv preprint arXiv:2410.22870, Abstract: Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at these detectors have played a pivotal role in shaping the design of future experiments and analyzing ongoing ones. However, the quest for accuracy in Large Hadron Collider (LHC) collisions comes at an imposing computational cost, with projections estimating the need for millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run 42. Simulating a single LHC event with Geant4 currently devours around 1000 CPU seconds, with simulations of the calorimeter subdetectors in particular imposing substantial computational demands 42. To address this challenge, we propose a conditioned quantum-assisted deep generative model. Our model integrates a conditioned variational autoencoder (VAE) on the exterior with a conditioned Restricted Boltzmann Machine (RBM) in the latent space, providing enhanced expressiveness compared to conventional VAEs. The RBM nodes and connections are meticulously engineered to enable the use of qubits and couplers on D-Wave’s Pegasus-structured \textit{Advantage} quantum annealer (QA) for sampling. We introduce a novel method for conditioning the quantum-assisted RBM using flux biases. We further propose a novel adaptive mapping to estimate the effective inverse temperature in quantum annealers. The effectiveness of our framework is illustrated using Dataset 2 of the CaloChallenge 41. ↩︎

  44. Calorimeter Surrogate Research, Geoffrey Fox University of Virginia, 2024 https://docs.google.com/document/d/19g0Avj9SYbVH7qSxoVUnnFKeGMuBdD9JCHVmBQB466M/ ↩︎

  45. Poster: https://drive.google.com/file/d/1PUiNDju_8N_wsDKI_W-g-jyCHb_5Hepo/ ↩︎

  46. Extended abstract: Correlation Frobenius Distance: A Metric for Evaluating Generative Models in Calorimeter Shower Simulation, Farzana Yasmin Ahmada, Vanamala Venkataswamya, Geoffrey Fox, University of Virginia, https://docs.google.com/document/d/1ndHkJY41_pHYZZne58B4_7HJQKTCxPzeMWVMJ0bsnOE ↩︎

Last modified March 17, 2025: add poster (a961f3d)